Combining predictive models of forgetting, relevance, and cost of interruption to guide automated reminding

ABSTRACT

The claimed matter provides systems and/or techniques that develop or use predictive models of human forgetting to effectuate automated reminding. The system includes the use of predictive models that infer the probability that aspects of items will be forgotten, models that evaluate the relevance of recalling aspects of items in different settings, based on contextual information related to user attributes associated with the items, and models of the context-sensitive cost of interrupting users with reminders. The system can combine the probability of users forgetting aspects of an item with an assessed cost of forgetting those aspects to ascertain expected costs for not being reminded about events, compare expected costs for not being reminded with expected costs for interrupting users, and based on comparisons between expected costs for being reminded and expected costs for interrupting users regarding events, generate and deliver reminder notifications to users about items.

BACKGROUND

It is well known that individuals tend to forget about such items astasks, locations, names, and events with regularity. One approach toreminding people about items that that they might forget is to employautomated reminding systems that alert users. For example, onlinecalendaring systems often provide services for alerting people aboutpending appointments. In the real world, there are many categories ofevents that may be forgotten, beyond appointments and it can be achallenge to inform a system about events that may be forgotten.Further, it can be a challenge to understand the times, and, moregenerally, the contexts, when information that might likely be forgottenmight be relevant. In addition, sending people reminders about itemsthat might be forgotten can be costly. People can be overwhelmed with amultitude of notifications and messages emitted from portable anddesktop computing systems and applications that have as their ostensibleimpetus and purported objective the making of daily life easier and moretolerable. Nonetheless, this unceasing flurry of notification has leftmost individuals overworked and frustrated with the cognitive load ofinterruptions and notifications. Recent studies have revealed that anaverage user or individual can receive several notifications every hourfrom a multitude of sources (e.g., e-mail, personal informationmanagers, etc.). Furthermore, it has also been revealed that eachnotification can require significant amounts of time to recover once theindividual has addressed and/or dispatched the issue associated with anotification (e.g., to regain one's thoughts and re-focus on the task athand). Clearly, where an individual receives scores of notificationsand/or reminders every hour and if, as has been posited, it can takesignificant amounts of time to realign or reorient one's cognition backto an interrupted task after a disruption, there can be insufficienthours in a day in which to complete one's own assignments and tasks.

The subject matter as claimed therefore is directed toward the automatedassistance with the recall of information that may be forgotten, whileaddressing the challenges of predicting forgetting, identifyingrelevance of information, and the cost of interruption associated withreminders, even when the reminders are overall valuable.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects of the disclosed subject matter. Thissummary is not an extensive overview, and it is not intended to identifykey/critical elements or to delineate the scope thereof. Its solepurpose is to present some concepts in a simplified form as a prelude tothe more detailed description that is presented later.

While reminders can be very useful, they nevertheless can be disruptive.The subject matter as claimed provides better prediction of forgetting,relevance of information to a context, and cost of interruption, so asto filter and schedule reminders, so as to maximize the benefits andminimize the disruptive effects of reminders. The matter as claimed anddisclosed herein provides intelligent notifications by simultaneouslyconsidering models of user's memory, interruption costs, relevance ofthe reminder, and/or ideal timing of the reminder based on contextualinformation.

To the accomplishment of the foregoing and related ends, certainillustrative aspects of the disclosed and claimed subject matter aredescribed herein in connection with the following description and theannexed drawings. These aspects are indicative, however, of but a few ofthe various ways in which the principles disclosed herein can beemployed and is intended to include all such aspects and theirequivalents. Other advantages and novel features will become apparentfrom the following detailed description when considered in conjunctionwith the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a machine-implemented system that develops and/oremploys predictive models of human memory to facilitate and effectuateautomated reminding in accordance with the claimed subject matter.

FIG. 2 provides a more detailed depiction of a machine-implementedmemory jogger component that constructs and/or utilizes predictivemodels of human memory to facilitate and effectuate automated remindingin accordance with an aspect of the claimed subject matter.

FIG. 3 provides a more detailed illustration of a machine-implementedmemory jogger component that establishes and/or employs predictivemodels of human memory to facilitate and effectuate automated anddynamic reminding in accordance with an aspect of the claimed subjectmatter.

FIG. 4 provides yet a further detailed illustration of amachine-implemented memory jogger component that builds and/or utilizespredictive models of human memory to facilitate and/or effectuateautomated and/or dynamic reminding in accordance with an aspect of theclaimed subject matter.

FIG. 5 provides illustration of a personalized Bayesian structureconstructed and/or employed in accordance with an aspect of the claimedsubject matter.

FIG. 6 depicts an illustrative hierarchical construct established and/orutilized in accordance with an aspect of the subject matter as claimed.

FIG. 7 illustrates a flow diagram of a machine implemented methodologythat establishes and/or utilizes predictive models of human memory tofacilitate and/or effectuate automated reminding in accordance with anaspect of the claimed subject matter.

FIG. 8 illustrates a block diagram of a computer operable to execute thedisclosed system in accordance with an aspect of the claimed subjectmatter.

FIG. 9 illustrates a schematic block diagram of an illustrativecomputing environment for processing the disclosed architecture inaccordance with another aspect.

DETAILED DESCRIPTION

The subject matter as claimed is now described with reference to thedrawings, wherein like reference numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding thereof. It may be evident, however, that theclaimed subject matter can be practiced without these specific details.In other instances, well-known structures and devices are shown in blockdiagram form in order to facilitate a description thereof.

Common experiences coupled with observations about the extensive use ofhardcopy notes, to-do lists, and computer-based reminder systemsdemonstrates that people forget about their tasks with great frequency.Computing devices are growing in popularity as aids to assist inremembering and recalling of tasks, appointments, important dates,locations, and names. Nevertheless, with many items and their associatedreminders spawning and creating messages and notifications, people canbecome overwhelmed with the cognitive workload placed upon them byinterruptions and notifications. Although notifications can be usefuland/or beneficial, these notifications are nonetheless often disruptiveand can adversely and deleteriously affect cognitive workload.Accordingly, the subject matter disclosed and claimed herein providesimproved filtering and scheduling of notifications and reminders inorder to minimize or mitigate their disruptive effects without reducingthe benefits of receiving such appropriate and timely notification andreminder.

The subject matter as claimed provides a design for a context-awarereminder system that can work as a personal filter that decides whichreminders are more useful to the individual than being disruptive to theindividual. The claimed subject matter utilizes probabilistic models tomodel the user and predict the expected value of a reminder. As part ofthe effective delivery of potentially valuable reminders, the matter asclaimed creates personal automated computational agents that can mediateif and when notifications should be delivered to users, and in so doing,utilizes predictive components that simultaneously estimate the costsassociated with interruption, predict the relevance of receivedreminders, predict the ability or inability of users to overall recallitems without a reminder, and/or predict the ability or inability ofusers to recollect different levels of detail or aspects of the items.Additionally, the claimed subject matter can reason about the idealtiming for delivering a reminder, taking into consideration one or morebits of contextual information. For example, an analysis of the idealtiming of a relevant reminder about the details of the location of ameeting at a distance away from a user can consider information aboutthe current and future traffic flows on roads between the currentlocation and the target location in order to better schedule thedispatch of reminder notifications.

Reminders can be useful to refresh an individual's memory about upcomingand/or impending events. A reminder can have associated therewith highlevel information to remind the individual that the event exists (e.g.,title and start time) or can provide details including estimated traveltime, estimated departure time, and/or directions. Remindernotifications nevertheless can cause disruption depending on theinterruptibility level of the individual and therefore can produceinterruption. The magnitude of the cost of interruption can depend onthe current attention and cognitive load of the individual. Anotification is useful if the benefit of the notification is higher thanthe interruption costs. Typically, one can estimate the value of areminder by subtracting the benefit of notification from theinterruption costs, and sending a reminder if the net value is estimatedto be positive.

While the claimed subject matter focuses on and is explicated in termsof event domains, it will be appreciated by those ordinarily cognizantin this field of endeavor that the matter as disclosed and claimedherein can find applicability or utility in other contexts of dailyhuman enterprise, and in particular, can be extended to other remindersettings. A reminder for an event or other items that may have beenforgotten (e.g., tasks that need to be completed, the names of peopleattending a party that might likely be forgotten, etc.) is beneficial ifthe item is actually relevant to the user in current or forthcomingcontexts. For example, a reminder about the existence of a forthcomingmeeting and/or the details about the event or item such as the location,topic, and attendees of the meeting might be most relevant if the userwishes to attend the event. A user can have a long list of potentiallyforgotten items or events with only a few events or items that are ofparticular interest to the user in a current or forthcoming setting.Thus, a relevance value can be utilized to aid in filtering relevantevents from irrelevant ones. Accordingly, the following discussionfocuses, without limitation, on events such as appointments andmeetings, but the reasoning and analysis set forth herein can apply in astraightforward manner to other items that may be forgotten.

In order to facilitate the integration of models of the relevance ofinformation in a setting, one can assume that a meeting is relevant if auser will attend the meeting. The probability of attending p(A|E) anevent (m) can be conditioned on some observational evidence E. Theprobability of attending p(A|E) can be estimated through use of aprobabilistic model that predicts attendance.

Events typically are not equally prioritized in a particular user'sschedule. One can consider events to be of a priority that reflects thecost of missing an event, with low priority events being associated byusers with a low cost of missing an event and high-priority events beingassociated by users with a high cost of missing the event. The priorityof an event can be predicted through use of a probabilistic model thattakes into consideration properties of the event as observationalevidence. Further, a predictive model can be employed to predict theexpected cost of interruption (ECI) of a user in a setting, based onobservational evidence about the setting. If the ECI is high for acurrent state, the user may prefer not to be reminded of a low priorityevent although she or he would be willing to be reminded for a highpriority event. The claimed subject matter provides a probabilisticmodel for inferring a probability distribution over the potentialpriorities of events. Thus, for any given event m, in accordance with anaspect, the claimed subject matter predicts the probability that m hashigh priority p(m^(H)), medium priority p(m^(M)), and low priorityp(m^(L)).

Events or other items are not simply forgotten versus remembered. Ratherthey can be recalled with different degrees of fidelity. For simplicityof illustration rather limitation, assume that the state of anindividual's memory about an event or other item can reside in one ofthree permissible states. Where the user has entirely forgotten that theevent or item even exists, the user can be classified as being in a“Forget All” state (FA), where the user remembers that the event or itemexists but nonetheless cannot remember details, such as location or timeof the event or item, such a user can be categorized as being in a“Forget Details” state (FD), and where the user remembers everythingabout the item or event, such a user can be designated as being in a“Remember” state (R). Based at least in part on these assumptions, thematter as disclosed and claimed herein can collect user annotated dataabout an individual's memory states with regard to previous items orevents (e.g., in the form of training data) and can utilize this data togenerate memory probabilistic models that can predict or infer p(FA|E),p(FD|E), p(R|E) probabilities given observational evidence E. Once againsolely for purposes of exposition rather than limitation, memory statesFA, FD, and R can be considered mutually exclusive and collectivelyexhaustive, therefore the probability that a user will remembereverything about a event given observational evidence E can berepresented by p(R|E)=1−p(FA|E)−p(FD|E).

Further, again for purposes elucidation rather than limitation, expectedutility values for reminding at any of the three illustrative memorystates can be represented as follows. In expected utility determinationone can follow the principles of expected utility decision making toascertain the Expected Utility of Reminding (EUR). For example,individuals can be requested to evaluate the value of time for threepossible cases; a minute cost for being late to high, medium, lowpriority events, represented as c_(Late) ^(H), c_(Late) ^(M), and/orc_(Late) ^(L); total cost for not attending a high, medium, low priorityevent, denoted as C_(NA) ^(H), C_(NA) ^(M) and/or C_(NA) ^(L); and theminute cost for being early, c. These values can represent theindividual's willingness to pay in monetary terms for not being in oneof the aforementioned situations or states. These user costs can then becombined with the probability of being in associated states to form theExpected Utility of Reminding (EUR) equation:

EUR=p(FA|E)^(U) _(R)(FA)+p(FD|E)U _(R)(FD)+p(R|E)U _(R)(R)  (1)

where U_(R)(FA), U_(R)(FD), and U_(R)(R) represent the estimated utilityof being reminded at states FA, FD, and/or R, respectively.

Moreover, solely for purposes of exposition rather than limitation, forutility determination the following assumptions and associatedformulations can be employed by the matter disclosed and claimed herein:if a event is relevant to the user then the user can be in the“Remember” state (R) and the user will typically be on time; if the useris on time, the utility of reminding the user to be on time (U_(OnTime))can be 0 (e.g., there is no point reminding the user about an event thathe or she is, or will be, on time for). The estimated utility of beingreminded in this state can be computed as:

U _(R)(R)=(p(A|E)(U _(OnTime) −U _(OT)))−ECI  (2)

Where the user has forgotten pertinent details regarding the event(e.g., event time, event location, etc.) the user can be categorized asbeing in the “Forgot Details” state (FD) and as such the user can belate by t minutes, with the corresponding estimated utility of beingreminded being: U_(Late)=−Cost_(Late)impending and/or approaching items or events, system 100 can predict,ascertain, and/or infer a user's probability for forgetting about anitem or event, combine the user's probability for forgetting about theevent or item with user specific criteria and/or costs to estimate orascertain a cost associated with not being reminded of the item orevent. System 100 can deliberate or balance between the cost for notbeing reminded versus the cost of interrupting the user from his or hercurrent tasks. Based at least in part on the decision, inference, and/ordeliberation made, system 100 can distribute or deliver (e.g., viaemail) a reminder notification to the user (e.g., if system 100ascertains that the cost of not being reminded outweighs the cost ofinterruption). It should be noted that system 100 can continuously,automatically, and/or dynamically adjust itself according to feedback,or lack thereof, received about the successes or failures of system 100.

As depicted, system 100 can include memory jogger component 102; anevent monitoring structure that can receive various and varied input andthat generates and utilizes Expected Utility of Reminding (EUR)determinations to disseminate or refrain from disseminatingnotifications to individual users of the system 100 based at least inpart on inputs elicited, generated, ascertained, and/or acquired. Memoryjogger component 102 can be in continuous and/or operative or sporadicand/or intermittent communications with observation devices 106 and/orpersonal information manager 108 via network topology or cloud 104.

As illustrated, memory jogger component 102 can be implemented entirelyin hardware and/or a combination of hardware and/or software inexecution. Further, memory jogger component 102 can be incorporatedwithin and/or associated with other compatible components. Additionally,memory jogger component 102 can be any type of machine that includes aprocessor and/or is capable of effective communications with networktopology or cloud 104. Illustrative machines that can comprise memoryjogger component 102 can include cell phones, smart phones, laptopcomputers, notebook computers, Tablet PCs, consumer and/or industrialdevices and/or appliances, hand-held devices, personal digitalassistants, server class machines and computing devices and/ordatabases, multimedia Internet enabled mobile phones, multimediaplayers, automotive components, avionics components, and the like.

Network topology or cloud 104 can include any viable communicationand/or broadcast technology, for example, wired and/or wirelessmodalities and/or

$\begin{matrix}{\begin{matrix}{{U_{R}({FD})} = {\left( {{p\left( A \middle| E \right)}\left( {U_{OnTime} - U_{Late}} \right)} \right) - {ECI}}} \\{= {\left( {{p\left( A \middle| E \right)}\left( {{- c_{Late}}t} \right)} \right) - {ECI}}}\end{matrix}{where}} & \begin{matrix}(3) \\(4)\end{matrix} \\{c_{Late} = {{{p\left( m^{H} \right)}c_{Late}^{H}} + {{p\left( m^{M} \right)}c_{Late}^{M}} + {{p\left( m^{L} \right)}c_{Late}^{L}}}} & (5)\end{matrix}$

Where the user has forgotten entirely about the event (e.g., is in the“Forget All” state), the user will typically miss the whole event andthe estimated utility of being reminded can be represented as:U_(NA)=−Cost_(NA)

$\begin{matrix}{\begin{matrix}{{U_{R}({FA})} = {\left( {{p\left( A \middle| E \right)}\left( {U_{OnTime} - U_{NA}} \right)} \right) - {ECI}}} \\{= {\left( {{p\left( A \middle| E \right)}\left( {- c_{NA}} \right)} \right) - {ECI}}}\end{matrix}{where}} & \begin{matrix}(6) \\(7)\end{matrix} \\{c_{NA} = {{{p\left( m^{H} \right)}c_{NA}^{H}} + {{p\left( m^{M} \right)}c_{NA}^{M}} + {{p\left( m^{L} \right)}c_{NA}^{L}}}} & (8)\end{matrix}$

Accordingly, the Expected Utility of Reminding (EUR) can be representedby:

EUR=p(A|E)(p(FA|E)c _(NA) +p(FD|E)c _(Late) t)−ECI  (9)

It should be noted in regard to the above formulations that ECI refersto the Expected Cost of Interruption (ECI).

Turning now to FIG. 1 that depicts a system 100 that utilizes predictivemodels of human memory to facilitate and effectuate automated remindingin accordance with an aspect of the claimed subject matter. As apreliminary but extremely cursory overview, system 100 can acquire orreceive impending or approaching events or items gleaned, for example,from a personal information manager and thereafter can evaluate theincoming events or items for relevance through use of a user's selfassessments about event attendance, for instance. Through utilization ofcontextual information and attributes associated with technologies canbe utilized to effectuate the claimed subject matter. Moreover, networktopology and/or cloud 104 can include utilization of Personal AreaNetworks (PANs), Local Area Networks (LANs), Campus Area Networks(CANs), Metropolitan Area Networks (MANs), extranets, intranets, theInternet, Wide Area Networks (WANs)—both centralized anddistributed—and/or any combination, permutation, and/or aggregationthereof. Additionally, network topology and/or cloud 104 can include orencompass communications or interchange utilizing Near-FieldCommunications (NFC) and/or communications utilizing electricalconductance through the human skin, for example.

Observation devices 106 can provide much contextual informationregarding a user's activities or inactivity, for example, low-level andhigh-level actions performed by the user, such as whether the user istyping or using the mouse, the current focus of the user, how frequentlythe user switches between applications, how many applications arevisited, the time periods during which the user is active or inactive,for instance. Observation devices 106 therefore can include a multitudeof various devices that can be in continuous and/or operative orsporadic and/or intermittent communication with memory jogger component102 and/or personal information manager 108 through network topology orcloud 104. Like memory jogger component 102, observation devices 106 canbe implemented entirely in hardware and/or as a combination of hardwareand/or software in execution. Moreover observation devices 106 caninclude any type of engine, machine, instrument of conversion, or modeof production that includes a processor and/or is capable of effectiveand operative communication with network topology or cloud 104.Illustrative instruments of conversion, modes of production, engines,mechanisms, devices, and/or machinery that can comprise observationdevices 106 can include desktop computers, server class computingdevices and/or databases, cell phones, smart phones, laptops, note bookcomputers, Tablet PCs, consumer and/or industrial devices and/orappliances and/or processes, hand-held devices, personal digitalassistants, multimedia Internet enabled mobile phones, or multimediaplayers. Observation devices 106 can also include microphones, motiondetectors, heat sensors, video cameras, keyboards, mouse, biometricfeedback components, and the like, that can be standalone items and/oraffiliated or confederated with the aforementioned and/or enumeratedinstruments of conversion, modes of production, engines, mechanisms,devices, etc.

Personal information manager 108 can include task and contentmanagement, note taking, journaling features, Web browsing, and/orcalendaring functionalities. Similar to memory jogger component 102 andobservation devices 106, personal information manager 108 can beimplemented entirely in hardware and/or as a combination of hardwareand/or software in execution. Further, personal information manager 108can be any type of mechanism, machine, device, facility, and/orinstrument that includes a processor and/or is capable of effectiveand/or operative communication with network topology or cloud 104.Mechanisms, machines, devices, facilities and/or instruments that cancomprise personal information manager 108 can include Tablet PCs, serverclass computing machines and/or databases, laptop computers, notebookcomputers, desktop computers, cell phones, smart phones, consumerappliances and/or instrumentation, industrial devices and/or components,hand-held devices, personal digital assistants, multimedia Internetenabled phones, multimedia players, automotive components, satellitesand/or satellite equipment, and the like.

FIG. 2 provides a more detailed depiction 200 of memory jogger component102 in accordance with an aspect of the claimed subject matter. Asillustrated memory jogger component 102 can actively or passivelyreceive and/or acquire input such as, for example, lists of events oritems (e.g., calendar events such as meetings, appointments, scheduledsporting events, concerts, recitals, etc.) from personal informationmanager 108. The lists of items or events (e.g., appointments, meetings,and the like) can be tentative or can have been accepted by the user.Additionally, each item or event can include other item or eventattributes, such as time, location, and possible attendees to theevents, for example. In order to generate and to be able to utilizeviable and useful predictive models, memory jogger component 102 canalso gather contextual information from observational sources such asobservation devices 106. For example, observation devices 106 can supplyinformation related to low level and/or high level actions performed bythe user as well as contextual data such as whether there is an ongoingconversation (e.g., telephone conversation) in the user's office and thepredicted duration of the detected conversation. These inputs can thenbe fed into components and/or aspects of the matter disclosed andclaimed herein and/or combined with user specific costs to evaluate theoverall benefit (e.g. Expected Utility of Reminding (EUR)) of sending orrefraining from sending reminder notifications to the user.

As illustrated, memory jogger component 102 can include interfacecomponent 202 (hereinafter referred to as “interface 202”) that canreceive and/or disseminate, communicate and/or partake in datainterchange with a plurality of disparate and diverse sources and/orcomponents. For instance, interface 202 can receive and/or transmit datato, and/or from, a multiplicity of devices or sources, such as, forexample, data associated with contextual information gleaned fromobservation devices 106 and/or information acquired or obtained frompersonal information manager 108 (e.g., items, events, . . . ).Additionally and/or alternatively, interface 202 can obtain and/orreceive data associated with a plethora of other information such as forexample, usernames and/or passwords, sets of encryption and/ordecryption keys, client applications, services, users, clients, devices,and/or entities involved with a particular transaction, portions oftransactions, and thereafter can convey the received or otherwiseacquired information to one or more of: cost component 204, attendancepredictor 206, priority predictor 208, memory predictor 210, or detailspredictor 212 for further analysis and/or processing. To facilitateand/or achieve its objectives, interface 202 can provide variousadapters, connectors, channels, communication pathways, etc. tointegrate the various components included in system 200, and moreparticularly, memory jogger component 102 into virtually any operatingsystem and/or database system and/or with one another. Additionallyand/or alternatively, interface 202 can provide various adapters,connectors, channels, communication modalities, and the like, that canprovide for interaction with various components that can comprise system200, and/or any other component (external and/or internal), data, andthe like, associated with system 200.

Memory jogger component 102 can further include cost component 204 thatcan predict costs of interrupting the user for a given time. Costcomponent 204 can implement or include both a training aspect and/or anactive aspect where during the training aspect busy palette windows canbe regularly, periodically or intermittently popped up or displayed. Thebusy palette windows can ask the user whether they are currently busy.During this training aspect, cost component 204 can also ask the user toenter user-specific cost values for interruptions. These cost-values canrepresent in monetary terms how much a user is willing to spend or payto avoid the disruptive effects of notifications. Cost component 204,also continuously, intermittently, automatically, or dynamically, canreceive high-level, low-level, and contextual signals about the currentstate of the user (e.g., from observation devices 106). For instance,low level activities or actions can include whether the user is typingor using the mouse, the user's current focus, and the user's history ofactivities or actions, for example. High-level activities or actions caninclude information associated with how frequently the user switchesbetween applications, how many applications are visited, or the timeperiods during which the user is active or inactive, for instance.Additionally, cost component 204 can collect, acquire, or receive, fromone or more other sources or repositories, local or remote, contextualinformation regarding upcoming items or events (e.g., appointments,meetings, etc.), conversations, and/or discussions that the user is, hasbeen or will be, involved in, or devices utilized.

In relation to the aforementioned training aspect, this aspect can allowusers to give feedback in real-time or offline (e.g., to a log ofemitted reminders persisted and/or associated with memory joggercomponent 102) about whether reminders were missing when needed, or ifissued, if the reminder had value or was inappropriate. Additionallyand/or alternatively, users can also provide feedback as to the reasonwhy the reminder was valueless or inappropriate, such reasons caninclude one or more of (1) the item or event had not been forgotten, (2)the wrong amount of detail (e.g., too much or too little) was specified,(3) the item or event was irrelevant, (4) the timing was too early ortoo late, and/or (5) the reminder was good but was received at a costlytime. This feedback can then be utilized to construct and/or extenddatabases that can be employed in building and/or refining variouspredictive models that can be utilized by the claimed matter.

With the aforementioned acquired, generated, or derived information,cost component 204 can construct predictive models that can be employedto estimate or infer a state of interruptibility of the user. Costcomponent 204 by combining a probability distribution of userinterruptibility with costs assigned by the user for each state ofinterruptibility, can determine an Expected Cost of Interruption (ECI).Given that a state of interruptibility can be denoted as, I_(i), thecorresponding costs can be represented as C(I_(i)) for an observationalstate E. Accordingly, an Expected Cost of Interruption (ECI) can bedetermined as the weighted sum over all possible states:

ECI=Σ_(i) p(I _(i) |E)C(I _(i))  (10)

Using a history of low-level observations, high-level observations,and/or contextual information, cost component 204 can construct orcompile a library of cases and apply Bayesian learningmodalities/techniques to build a personalized Bayesian structure ornetwork that can be utilized to ascertain a cost of interruption (e.g.,see FIG. 5 for an illustrative personalized Bayesian network construct).

Memory jogger component 102 can further include attendance predictor 206that can predict the probability of a user attending a particular event.Attendance predictor 206 can keep record of, log, tabulate, chronicle,archive, or document all events persisted on or associated with aspectsof personal information manager 108 (e.g. calendar events, appointments,. . . ) as well as other properties that can be manifest and affiliatedwith those aspects of personal information manager 108 recorded,chronicled, or logged, such as, for example, event time and date,duration, subject, location, organizer of the event, number of invitees,role of the user in relation to the event, identities of otherattendees, whether the event is recurrent, and/or marked as busy:Attendance predictor 206 can also incorporate hierarchical relationshipsof attendees (e.g. from organizational charts). Attendance predictor 206in accordance with an aspect of the matter disclosed can construct formsthat can list events and fields for marking attendance of attendees. Theforms established or created by attendance predictor 206 can becompleted either according to a basic machine or user initiatedheuristic, with assistance from a user, in accordance with somecombination of machine or user heuristic, user input, and/or artificialintelligence or machine learning techniques. Completed forms canthereafter be employed by attendance component 206 to build anindividuated Bayesian network or structure that can be utilized bysystem 200 to predict the probability of attendance.

Memory jogger component 102 can further include priority predictor 208that can be employed to classify events according to priority (e.g.,high, medium, low). Priority predictor 208 in accordance with an aspectof the claimed subject matter can develop a form that can have eventattributes with affiliated fields for labeling if an event has anobjective (e.g., from the perspective of the organization or otherparticipants invited or attending the event) and/or subjective (e.g.,from the perspective of the individual user) priority. The developedform typically can be completed by individual users, but machinelearning, artificial intelligence, and/or machine or user developedheuristics can also be implemented with equal effect and applicability.Priority predictor 208 can utilize the data included in these forums togenerate Bayesian networks for predicting or prognosticating priorityvalues.

Typically, a reminder is only useful provided it refreshes users' memoryabout something that has been forgotten. Generally, very importantevents or items do not require reminders to be generated and sent.Otherwise, provided that the user remembers that an event or item is tooccur or details associated with the item or event (e.g., time,location, etc.) that is all that is typically required to refresh auser's memory. Thus, memory jogger component 102 utilizes thisobservation to model those aspects that individuals typically forget.Accordingly, memory jogger component 102 utilizes memory predictor 210and details predictor 212 to predict whether or not a user remembersthat there is an approaching event and to check to see whether the userremembers the important and/or pertinent details regarding the event oritem (e.g., location, subject, or date and time of the event or item).

Memory predictor 210 and/or details predictor 212 can employ supervisedlearning to predict the memory status of a user. For example, memorypredictor 210 and/or details predictor 212 can develop, construct,and/or utilize forms that allow users to enter values for particularmemory states. Typically, the forms developed, constructed, and/oremployed, for example, can include, lists of events sorted in order ofoccurrence with associated fields that can be utilized to enter booleanvalues for remembering. Through the developed and/or constructed form,users can see how many events they have scheduled on any particular day,previous and future events, and/or time intervals between events. Inthis manner the user can remember his or her cognitive load for aparticular day and annotate the forms accordingly and accurately.

Annotated forms can thereafter be employed by memory predictor 210and/or details predictor to 212 to produce models that predict thelikelihood that the user will forget about the event itself or forgetabout details of the event. Both memory predictor 210 and/or detailspredictor 212 can employ similar features for analysis such as, forexample, time and date, subject, attendee list, location, organizer,role of the user, whether the event is recurrent or not, whether theevent or time has already been indicated as being busy, etc.Hierarchical relationships between the attendees can also be obtainedand/or automatically established/constructed and utilized in theprognosticative determination effectuated by memory predictor 210 and/ordetails predictor 212. Additionally, determination as to whether theattendees, organizers, and/or the location of the event are atypicalgiven the history of other events in the recorded or chronicled past,present, or future, can also be made and utilized.

Memory predictor 210 and/or details predictor 212 can employ theaforementioned gathered information to construct Bayesian structures ornetworks to predict or infer the probability that the user will forgetabout the event entirely (e.g., “Forget All”), and/or the probabilitythat the user will forget about details (e.g., “Forget Details”)associated with the event (e.g., the user remembers the event itself butnevertheless forgets pertinent details related to the event). Further,memory predictor 210 and/or details predictor 212 can employ theBayesian construct to produce a decision tree (e.g., see FIG. 6) thatcan be utilized to isolate and identify key influencing variables as towhether the user forgets about an event or details associated with theevent. Some illustrative influencing variables that can be identifiedand utilized by memory predictor 210 and/or details predictor 212 caninclude response status, location, time, day of the event, the event'srecurrence pattern, the relationship of the user to the event (e.g.,organizer, event leader, keynote speaker, etc.), and/or the busy statusassociated with the event. Other influencing details that can also beemployed, especially in connection with details predictor 212, caninclude whether event details were delivered via a mailing list, forinstance.

FIG. 3 provides a further depiction 300 of memory jogger component 102.As illustrated memory jogger component 102 can include all thecomponents and/or aspects elucidated in connection with FIG. 2 (e.g.,interface component 202, cost component 204, attendance predictor 206,priority predicted 208, memory predictor 210, and details predictor212). Because much of the configuration and operation of thesecomponents and/or aspects are substantially similar to those presentedand detailed in connection with FIG. 2, a detailed description of suchfeatures has been omitted to avoid needless repetition and for the sakeof brevity and conciseness of exposition. Nevertheless, memory joggercomponent 102 as illustrated in FIG. 3 can additionally include trafficcomponent 302 that can provide prognostications related to live trafficconditions, traveling routes, and/or traveling durations.

As will be appreciated, recollecting an event, its details and/orattendance at the event can involve aspects other than the event itselfand/or its associated detail. An additional aspect that can determinewhether or not a user genuinely remembers an event and/or its associateddetails or conveniently forgets or consciously avoids the event can becurrent traveling conditions or lengthy travel duration, for example.Accordingly, the matter as claimed and disclosed herein can includetraffic predictor 302 that provides live traffic information to beemployed in the Expected Utility of Reminding (EUR) determination. Thus,the notifications disseminated and/or distributed by the claimed subjectmatter can aid users to arrive at events on time by deliveringinformation such as, for example, live traffic predictions, drivingdirections, route adjustments, estimated time of arrival, and the like.

Traffic predictor 302 can provide context-sensitive traffic predictionsbased at least in part on current and/or future flows in a trafficsystem. Traffic predictor 302 can employ incident reports, date and timeof day, contextual data including holiday schedules, whether or notschool is in session, or whether or not there exists large-scale socialevents, such as sporting events or music concerts. For a givendestination therefore traffic predictor 302 can determine the expectedtravel time, determine live travel duration (Δt_(live)) in minutes, forinstance, accompanied with an estimate (Δt_(expected)) under regulartraffic conditions. The difference between Δt_(live) and Δt_(expected)can be employed as being the estimated error in user's prediction fortraffic conditions (e.g., Δt=Δt_(live)−Δt_(expected)).

The estimated error (or traffic prediction error) can affect theexpected time that the user will be late to a given event. For instance,if a road heading to the event is congested due to an accident and thelive travel estimate deduced by traffic predictor 302 is 10 minutes morethan ordinarily expected, the estimated costs for forgetting details canbe commensurately and dynamically or automatically adjusted to reflectthis delay (e.g. an extra 10 minutes should be included with the costfor being late). Thus, the raw assumption that a user will always be ontime because the user remembers every detail about the event (e.g.,remembers that there is an event and the details associated with theevent) can often be faulty. Without notification of traffic conditions(e.g., as provided by traffic predictor 302), a user departing inaccordance with an expected traffic flow will be late and will arrive atthe event 10 minutes late in the foregoing example. Consequently, theclaimed matter can employ modified Expected Utility of Reminding (EUR)formulations and/or determinations to take these situations into accountand in so doing can utilize the following equivalences:

EUR=p(A|E)(p(FA|E)c _(NA) +p(FD|E)C(FD,E)+p(R)C(R,E))−ECI  (11)

The cost function for the memory state of forgetting about the details(FD) of an event can be represented by C(FD, E) when t is how manyminutes the user is late due to forgetting about the event, and cdenotes the minimum cost for being early, and as such the followingformulation can be employed by traffic predictor 302:

$\begin{matrix}{{C\left( {{FD},E} \right)} = \left\{ \begin{matrix}{\left( {t + {\Delta \; t}} \right)c_{Late}} & {{{if}\mspace{14mu} \Delta \; t} > 0} \\{\left( {t + {\Delta \; t}} \right)c_{Late}} & {{{if}\mspace{14mu} \Delta \; t} \leq {0\mspace{14mu} {and}\mspace{14mu} \left( {t + {\Delta \; t}} \right)} > 0} \\{\left( {{- t} + {\Delta \; t}} \right)c} & {{{if}\mspace{14mu} \Delta \; t} \leq {0\mspace{14mu} {and}\mspace{14mu} \left( {t + {\Delta \; t}} \right)} \leq 0}\end{matrix} \right.} & (12)\end{matrix}$

Moreover, the cost function for remembering about the event can bedefined and utilized by traffic predictor 302 as follows. It shouldnevertheless be noted, without limitation, that the cost functiontypically corresponds with the value of delivering live trafficpredictions to the user.

$\begin{matrix}{{C\left( {R,E} \right)} = \left\{ \begin{matrix}{\Delta \; {tc}_{late}} & {{{if}\mspace{14mu} \Delta \; t} > 0} \\{\left( {{- \Delta}\; t} \right)c} & {otherwise}\end{matrix} \right.} & (13)\end{matrix}$

FIG. 4 provides yet a further illustration 400 of memory joggercomponent 102. Once again as illustrated, memory jogger component 102can include all the components and/or aspects elucidated in connectionwith FIGS. 2 and 3 (e.g., interface component 202, cost component 204,attendance predictor 206, priority predicted 208, memory predictor 210,details predictor 212, and traffic predictor 302). Since much of theconfiguration and operation of these components and/or aspects aresubstantially similar to those presented and recited in connection withFIGS. 2 and 3, a detailed description of such features has been omittedto avoid needless repetition and for the sake of brevity and concisenessof exposition. Nevertheless, memory jogger component 102, as illustratedin FIG. 4, can additionally include timing component 402 that cansynchronize distribution or dissemination of reminder notifications tomaximize the benefits users can obtain from utilization of remindersystems.

Thus, timing component 402 distributes and/or disseminates remindernotifications to user in a concordant manner so as to maximize thebenefit that users can receive from employing the claimed subjectmatter. The Expected Utility of Reminding (EUR) can change in time asExpected Cost of Interruption (ECI) changes. For example, a user talkingon the phone may not be able to receive a notification, but cannevertheless benefit from receiving the notification on completion ofhis or her phone conversation. In order to attain this objective, theclaimed subject matter through timing component 402 can search to find,without limitation, moments that satisfy one or more of the followingconditions: that Expected Utility of Reminding (EUR) is positive; theuser has sufficient time to get to the event location after receivingreminder notifications; or the timing of the reminder is as close to theexpected departure time as possible so that the user's memory about theevent is kept fresh until departure time.

Timing component 402 can predict the duration of travel (T(t)) to anevent location given the departure time t, inclusive in the departuretime t can be the time required for the user to leave from a currentlocation, get to a mode of transportation (e.g., public transportation,automobile, . . . ) and start the trip (Δt_(s)) and the time necessaryto leave the mode of transportation (e.g., park the automobile, oregress from the public transportation system, etc.) and arrive at theexact destination (Δt_(e)). For the sake of exposition rather thanlimitation, the optimal time to commence the trip can be denoted ast*(e.g. the latest time t that satisfies t_(m)−Δt_(e)−T(t)−t≧0).Similarly, the latest time that the user can be reminded so that theuser can arrive at the event on time ist_(max)=t_(m)−Δt_(s)−Δt_(e)−T(t−t_(s)). Moreover, where the user doesnot receive live predictions of travel duration, the expected departuretime can be represented as:t_(expected)=t_(m)−Δt_(expected)−Δt_(s)−Δt_(e).

In view of the foregoing, timing component 402 should consider at leastthe following two cases in order to time the dissemination of remindernotifications appropriately. Where the expected travel duration ishigher than the live estimate, the user should be reminded beforet_(expected) so that the user can postpone departure time to t*. Theuser can be reminded by timing component 402 at the nearest time (t) tot_(expected) that satisfies EUR(t)>0. The user thus is given the liveestimate and the recommended departure time t*. Where the live estimateis higher than the expected travel duration, the user should berecommended to leave earlier than t_(expected) in order to be on time.The user can be reminded by timing component 402 at the nearest time tto t_(max) that satisfies EUR(t)>0 and recommended to depart at t*.

Further, timing component 402 can schedule reminders so that theExpected Utility of Reminding (EUR) values are maximized. For example,the user may prefer to be reminded a few minutes earlier than t_(max)where the Expected Utility of Reminding (EUR) value improvessignificantly. Nevertheless, the time that maximizes the ExpectedUtility of Reminding (EUR) may not be the optimal time to remind theuser as the user will be more likely to forget again especially wherethe time between sending the reminder and t increases. As will have beenobserved there can be different moments in time in terms of the ExpectedUtility of Reminding (EUR) values and the probability of forgettingagain can be determinant of the time for reminder t_(r). Accordingly,the utility of a reminder at time t can be combined with a discountingfactor that typically is inversely proportional to (t*−t). Therefore,time for reminder t_(r) can be determined by timing component 402 basedat least in part on discounted Expected Utility of Reminding (EUR_(d))values formulated as follows, where c represents a constant thatcontrols how the utility of a reminder falls as (t*−t) increases.

EUR_(d)(t)=e ^(c(t−t*))EUR(t)  (14)

t _(r)=max_(t) {e ^(c(t−t*))EUR(t):EUR(t)>0,t*>t}  (15)

FIG. 5 exemplifies an illustrative personalized Bayesian structure 500utilized to model and/or predict whether or not a user has or willforget completely about an event. Typically, the illustratedpersonalized Bayesian network 500 can be constructed from data annotatedby a user from previous calendar items and events. It should be noted,and as will be appreciated by those cognizant in the art, that only asmall fraction of the personalize Bayesian structure 500 has beendepicted and that in actuality the network 500 would include many morevertices and/or edges.

FIG. 6 depicts an illustrative hierarchical structure 600 that can beemployed as a decision tree to model and prognosticate upon whether auser will or will not forget completely about an event. Generally, sucha hierarchical structure 600 can be constructed from raw user annotateddata related to previous calendar events and/or items, and/or can bedeveloped from a previously constructed Bayesian structure such as thatdepicted in FIG. 5. Only a small fraction of the hierarchical structure600 has been represented in FIG. 6 and as will be readily appreciated bythose skilled in this undertaking, hierarchical structure 600 can bemany multiple layers and/or levels deep and wide.

In view of the illustrative systems shown and described supra,methodologies that may be implemented in accordance with the disclosedsubject matter will be better appreciated with reference to the flowchart of FIG. 7. While for purposes of simplicity of explanation, themethodologies are shown and described as a series of blocks, it is to beunderstood and appreciated that the claimed subject matter is notlimited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methodologies described hereinafter.Additionally, it should be further appreciated that the methodologiesdisclosed hereinafter and throughout this specification are capable ofbeing stored on an article of manufacture to facilitate transporting andtransferring such methodologies to computers.

The claimed subject matter can be described in the general context ofcomputer-executable instructions, such as program modules, executed byone or more components. Generally, program modules can include routines,programs, objects, data structures, etc. that perform particular tasksor implement particular abstract data types. Typically the functionalityof the program modules may be combined and/or distributed as desired invarious aspects.

FIG. 7 illustrates a machine implemented methodology 700 that constructsand utilizes predictive models of human memory to facilitate andeffectuate automated reminding in accordance with an aspect of theclaimed subject matter. Method 700 can commence at 702 where events oritems from a personal information management source can be passively oractively acquired or received. At 704 each event or item received oracquired can be evaluated for relevance by using a user's selfassessments about the event and sentiments with respect to attending theevent or regarding the item. At 706 the methodology can use contextualinformation and attributes associated with the item or approaching eventto predict the probability that the user will forget about the event oritem. At 708 the predicted probability that the user will forget aboutthe event or item can be combined with a determined cost of not beingreminded about the item or event. At 710 a comparison can be made withrespect to the cost of non-reminder (e.g., the cost of not beingreminded about an event or item) and a determined cost of interruption(e.g., Expected Cost of Interruption (ECI)). Based at least in part onthe comparison effectuated and carried out at 710 and where it is foundto be beneficial to interrupt the user, at 712 reminder messages and/ornotifications can be disseminated or delivered to recipients, forexample, via email.

The claimed subject matter can be implemented via object orientedprogramming techniques. For example, each component of the system can bean object in a software routine or a component within an object. Objectoriented programming shifts the emphasis of software development awayfrom function decomposition and towards the recognition of units ofsoftware called “objects” which encapsulate both data and functions.Object Oriented Programming (OOP) objects are software entitiescomprising data structures and operations on data. Together, theseelements enable objects to model virtually any real-world entity interms of its characteristics, represented by its data elements, and itsbehavior represented by its data manipulation functions. In this way,objects can model concrete things like people and computers, and theycan model abstract concepts like numbers or geometrical concepts.

As used in this application, the terms “component” and “system” areintended to refer to a computer-related entity, either hardware, acombination of hardware and software, or software in execution. Forexample, a component can be, but is not limited to being, a processrunning on a processor, a processor, a hard disk drive, multiple storagedrives (of optical and/or magnetic storage medium), an object, anexecutable, a thread of execution, a program, and/or a computer. By wayof illustration, both an application running on a server and the servercan be a component. One or more components can reside within a processand/or thread of execution, and a component can be localized on onecomputer and/or distributed between two or more computers.

Machine learning and reasoning systems, including those that useexplicitly and/or implicitly trained statistical classifiers can beemployed in connection with performing inference and/or probabilisticdeterminations and/or statistical-based determinations as in accordancewith one or more aspects of the claimed subject matter as describedhereinafter. As used herein, the term “inference,” “infer” or variationsin form thereof refers generally to the process of reasoning about orinferring states of the system, environment, and/or user from a set ofobservations as captured via events and/or data. Inference can beemployed to identify a specific context or action, or can generate aprobability distribution over states, for example. The inference can beprobabilistic—that is, the computation of a probability distributionover states of interest based on a consideration of data and events.Inference can also refer to techniques employed for composinghigher-level events from a set of events and/or data. Such inferenceresults in the construction of new events or actions from a set ofobserved events and/or stored event data, whether or not the events arecorrelated in close temporal proximity, and whether the events and datacome from one or several event and data sources. Various classificationschemes and/or systems (e.g., support vector machines, non-linearclassification methodologies, including methods referred to as neuralnetwork methodologies, Bayesian belief networks and other probabilisticgraphical models, and other methods including the use of associationrules mined from databases, etc.) can be employed in connection withperforming automatic and/or inferred action in connection with theclaimed subject matter.

Furthermore, all or portions of the claimed subject matter may beimplemented as a system, method, apparatus, or article of manufactureusing standard programming and/or engineering techniques to producesoftware, firmware, hardware or any combination thereof to control acomputer to implement the disclosed subject matter. The term “article ofmanufacture” as used herein is intended to encompass a computer programaccessible from any computer-readable device or media. For example,computer readable media can include but are not limited to magneticstorage devices (e.g., hard disk, floppy disk, magnetic strips . . . ),optical disks (e.g., compact disk (CD), digital versatile disk (DVD) . .. ), smart cards, and flash memory devices (e.g., card, stick, key drive. . . ). Additionally it should be appreciated that a carrier wave canbe employed to carry computer-readable electronic data such as thoseused in transmitting and receiving electronic mail or in accessing anetwork such as the Internet or a local area network (LAN). Of course,those skilled in the art will recognize many modifications may be madeto this configuration without departing from the scope or spirit of theclaimed subject matter.

Some portions of the detailed description have been presented in termsof algorithms and/or symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions and/orrepresentations are the means employed by those cognizant in the art tomost effectively convey the substance of their work to others equallyskilled. An algorithm is here, generally, conceived to be aself-consistent sequence of acts leading to a desired result. The actsare those requiring physical manipulations of physical quantities.Typically, though not necessarily, these quantities take the form ofelectrical and/or magnetic signals capable of being stored, transferred,combined, compared, and/or otherwise manipulated.

It has proven convenient at times, principally for reasons of commonusage, to refer to these signals as bits, values, elements, symbols,characters, terms, numbers, or the like. It should be borne in mind,however, that all of these and similar terms are to be associated withthe appropriate physical quantities and are merely convenient labelsapplied to these quantities. Unless specifically stated otherwise asapparent from the foregoing discussion, it is appreciated thatthroughout the disclosed subject matter, discussions utilizing termssuch as processing, computing, calculating, determining, and/ordisplaying, and the like, refer to the action and processes of computersystems, and/or similar consumer and/or industrial electronic devicesand/or machines, that manipulate and/or transform data represented asphysical (electrical and/or electronic) quantities within the computer'sand/or machine's registers and memories into other data similarlyrepresented as physical quantities within the machine and/or computersystem memories or registers or other such information storage,transmission and/or display devices.

Referring now to FIG. 8, there is illustrated a block diagram of acomputer operable to execute the disclosed system. In order to provideadditional context for various aspects thereof, FIG. 8 and the followingdiscussion are intended to provide a brief, general description of asuitable computing environment 800 in which the various aspects of theclaimed subject matter can be implemented. While the description aboveis in the general context of computer-executable instructions that mayrun on one or more computers, those skilled in the art will recognizethat the subject matter as claimed also can be implemented incombination with other program modules and/or as a combination ofhardware and software.

Generally, program modules include routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the inventive methods can be practiced with other computer systemconfigurations, including single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

The illustrated aspects of the claimed subject matter may also bepracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

A computer typically includes a variety of computer-readable media.Computer-readable media can be any available media that can be accessedby the computer and includes both volatile and non-volatile media,removable and non-removable media. By way of example, and notlimitation, computer-readable media can comprise computer storage mediaand communication media. Computer storage media includes both volatileand non-volatile, removable and non-removable media implemented in anymethod or technology for storage of information such ascomputer-readable instructions, data structures, program modules orother data. Computer storage media includes, but is not limited to, RAM,ROM, EEPROM, flash memory or other memory technology, CD-ROM, digitalvideo disk (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by the computer.

With reference again to FIG. 8, the illustrative environment 800 forimplementing various aspects includes a computer 802, the computer 802including a processing unit 804, a system memory 806 and a system bus808. The system bus 808 couples system components including, but notlimited to, the system memory 806 to the processing unit 804. Theprocessing unit 804 can be any of various commercially availableprocessors. Dual microprocessors and other multi-processor architecturesmay also be employed as the processing unit 804.

The system bus 808 can be any of several types of bus structure that mayfurther interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 806 includesread-only memory (ROM) 810 and random access memory (RAM) 812. A basicinput/output system (BIOS) is stored in a non-volatile memory 810 suchas ROM, EPROM, EEPROM, which BIOS contains the basic routines that helpto transfer information between elements within the computer 802, suchas during start-up. The RAM 812 can also include a high-speed RAM suchas static RAM for caching data.

The computer 802 further includes an internal hard disk drive (HDD) 814(e.g., EIDE, SATA), which internal hard disk drive 814 may also beconfigured for external use in a suitable chassis (not shown), amagnetic floppy disk drive (FDD) 816, (e.g., to read from or write to aremovable diskette 818) and an optical disk drive 820, (e.g., reading aCD-ROM disk 822 or, to read from or write to other high capacity opticalmedia such as the DVD). The hard disk drive 814, magnetic disk drive 816and optical disk drive 820 can be connected to the system bus 808 by ahard disk drive interface 824, a magnetic disk drive interface 826 andan optical drive interface 828, respectively. The interface 824 forexternal drive implementations includes at least one or both ofUniversal Serial Bus (USB) and IEEE 1094 interface technologies. Otherexternal drive connection technologies are within contemplation of theclaimed subject matter.

The drives and their associated computer-readable media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 802, the drives and mediaaccommodate the storage of any data in a suitable digital format.Although the description of computer-readable media above refers to aHDD, a removable magnetic diskette, and a removable optical media suchas a CD or DVD, it should be appreciated by those skilled in the artthat other types of media which are readable by a computer, such as zipdrives, magnetic cassettes, flash memory cards, cartridges, and thelike, may also be used in the illustrative operating environment, andfurther, that any such media may contain computer-executableinstructions for performing the methods of the disclosed and claimedsubject matter.

A number of program modules can be stored in the drives and RAM 812,including an operating system 830, one or more application programs 832,other program modules 834 and program data 836. All or portions of theoperating system, applications, modules, and/or data can also be cachedin the RAM 812. It is to be appreciated that the claimed subject mattercan be implemented with various commercially available operating systemsor combinations of operating systems.

A user can enter commands and information into the computer 802 throughone or more wired/wireless input devices, e.g., a keyboard 838 and apointing device, such as a mouse 840. Other input devices (not shown)may include a microphone, an IR remote control, ajoystick, a game pad, astylus pen, touch screen, or the like. These and other input devices areoften connected to the processing unit 804 through an input deviceinterface 842 that is coupled to the system bus 808, but can beconnected by other interfaces, such as a parallel port, an IEEE 1094serial port, a game port, a USB port, an IR interface, etc.

A monitor 844 or other type of display device is also connected to thesystem bus 808 via an interface, such as a video adapter 846. Inaddition to the monitor 844, a computer typically includes otherperipheral output devices (not shown), such as speakers, printers, etc.

The computer 802 may operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 848. The remotecomputer(s) 848 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallyincludes many or all of the elements described relative to the computer802, although, for purposes of brevity, only a memory/storage device 850is illustrated. The logical connections depicted include wired/wirelessconnectivity to a local area network (LAN) 852 and/or larger networks,e.g., a wide area network (WAN) 854. Such LAN and WAN networkingenvironments are commonplace in offices and companies, and facilitateenterprise-wide computer networks, such as intranets, all of which mayconnect to a global communications network, e.g., the Internet.

When used in a LAN networking environment, the computer 802 is connectedto the local network 852 through a wired and/or wireless communicationnetwork interface or adapter 856. The adaptor 856 may facilitate wiredor wireless communication to the LAN 852, which may also include awireless access point disposed thereon for communicating with thewireless adaptor 856.

When used in a WAN networking environment, the computer 802 can includea modem 858, or is connected to a communications server on the WAN 854,or has other means for establishing communications over the WAN 854,such as by way of the Internet. The modem 858, which can be internal orexternal and a wired or wireless device, is connected to the system bus808 via the serial port interface 842. In a networked environment,program modules depicted relative to the computer 802, or portionsthereof, can be stored in the remote memory/storage device 850. It willbe appreciated that the network connections shown are illustrative andother means of establishing a communications link between the computerscan be used.

The computer 802 is operable to communicate with any wireless devices orentities operatively disposed in wireless communication, e.g., aprinter, scanner, desktop and/or portable computer, portable dataassistant, communications satellite, any piece of equipment or locationassociated with a wirelessly detectable tag (e.g., a kiosk, news stand,restroom), and telephone. This includes at least Wi-Fi and Bluetooth™wireless technologies. Thus, the communication can be a predefinedstructure as with a conventional network or simply an ad hoccommunication between at least two devices.

Wi-Fi, or Wireless Fidelity, allows connection to the Internet from acouch at home, a bed in a hotel room, or a conference room at work,without wires. Wi-Fi is a wireless technology similar to that used in acell phone that enables such devices, e.g., computers, to send andreceive data indoors and out; anywhere within the range of a basestation. Wi-Fi networks use radio technologies called IEEE 802.11x (a,b, g, etc.) to provide secure, reliable, fast wireless connectivity. AWi-Fi network can be used to connect computers to each other, to theInternet, and to wired networks (which use IEEE 802.3 or Ethernet).

Wi-Fi networks can operate in the unlicensed 2.4 and 5 GHz radio bands.IEEE 802.11 applies to generally to wireless LANs and provides 1 or 2Mbps transmission in the 2.4 GHz band using either frequency hoppingspread spectrum (FHSS) or direct sequence spread spectrum (DSSS). IEEE802.11a is an extension to IEEE 802.11 that applies to wireless LANs andprovides up to 54 Mbps in the 5 GHz band. IEEE 802.11a uses anorthogonal frequency division multiplexing (OFDM) encoding scheme ratherthan FHSS or DSSS. IEEE 802.11b (also referred to as 802.11 High RateDSSS or Wi-Fi) is an extension to 802.11 that applies to wireless LANsand provides 11 Mbps transmission (with a fallback to 5.5, 2 and 1 Mbps)in the 2.4 GHz band. IEEE 802.11g applies to wireless LANs and provides20+Mbps in the 2.4 GHz band. Products can contain more than one band(e.g., dual band), so the networks can provide real-world performancesimilar to the basic 10BaseT wired Ethernet networks used in manyoffices.

Referring now to FIG. 9, there is illustrated a schematic block diagramof an illustrative computing environment 900 for processing thedisclosed architecture in accordance with another aspect. The system 900includes one or more client(s) 902. The client(s) 902 can be hardwareand/or software (e.g., threads, processes, computing devices). Theclient(s) 902 can house cookie(s) and/or associated contextualinformation by employing the claimed subject matter, for example.

The system 900 also includes one or more server(s) 904. The server(s)904 can also be hardware and/or software (e.g., threads, processes,computing devices). The servers 904 can house threads to performtransformations by employing the claimed subject matter, for example.One possible communication between a client 902 and a server 904 can bein the form of a data packet adapted to be transmitted between two ormore computer processes. The data packet may include a cookie and/orassociated contextual information, for example. The system 900 includesa communication framework 906 (e.g., a global communication network suchas the Internet) that can be employed to facilitate communicationsbetween the client(s) 902 and the server(s) 904.

Communications can be facilitated via a wired (including optical fiber)and/or wireless technology. The client(s) 902 are operatively connectedto one or more client data store(s) 908 that can be employed to storeinformation local to the client(s) 902 (e.g., cookie(s) and/orassociated contextual information). Similarly, the server(s) 904 areoperatively connected to one or more server data store(s) 910 that canbe employed to store information local to the servers 904.

What has been described above includes examples of the disclosed andclaimed subject matter. It is, of course, not possible to describe everyconceivable combination of components and/or methodologies, but one ofordinary skill in the art may recognize that many further combinationsand permutations are possible. Accordingly, the claimed subject matteris intended to embrace all such alterations, modifications andvariations that fall within the spirit and scope of the appended claims.Furthermore, to the extent that the term “includes” is used in eitherthe detailed description or the claims, such term is intended to beinclusive in a manner similar to the term “comprising” as “comprising”is interpreted when employed as a transitional word in a claim.

1. A system implemented on a machine that develops or utilizespredictive models of human memory to effectuate or facilitate automatedreminding, comprising: a component that acquires an event or other itemvia an interface, the component evaluates the event or other item forrelevance based at least in part on a predictive model for the relevanceof the event or other item based on contextual information or attributesassociated with the event or other item, the component also infers aprobability of the user forgetting about the event or other item,combines the probability of the user forgetting about the event or otheritem with a user specific cost of forgetting to ascertain an expectedcost for not being reminded, the component compares the expected costfor not being reminded with an expected cost for interrupting the user,based at least in part on the comparison between the expected cost forbeing reminded and the expected cost for interrupting the user thecomponent generates and delivers a reminder notification to the user. 2.The system of claim 1, the user assessment of the event or other itembased at least in part on a user evaluated cost that includes a monetaryvaluation that the user assigns to avoiding a disruptive effectassociated with receiving a reminder notification.
 3. The system ofclaim 1, the component at least one of continuously or intermittentlyreceives contextual evidence regarding a current state affiliated withthe user.
 4. The system of claim 3, where evidence about a user's stateincludes user application context switches, user dwell periods within anapplication, periods of user activity, utilization of a mouse, akeyboard, or a history of the user's computational activity orinactivity.
 5. The system of claim 3, where evidence includes a user'slocation, velocity, or proximity to people, resources, or structures. 6.The system of claim 3, the evidence about state including dataassociated with one or more events or items, the one or more events oritems including appointments, conversations, discussions that the useris one or more of currently involved in, will be involved in, or hasbeen involved in, or devices utilized by the user in one or more oftaking part in the one or more events, has been employed to take part inthe one or more events, or will be employed to take part in the one ormore events.
 7. The system of claim 1, the component determines anexpected cost of interruption (ECI) based at least in part on acombination of a probability distribution of user interruptibility withcosts assigned by a user for each state of interruptibility.
 8. Thesystem of claim 1, the component categorizes events into one or morepriority levels based at least in part on a probabilistic model.
 9. Thesystem of claim 1, the component based at least in part on a history ofhigh-level observations, low-level observations, or contextualinformation establishes a library of cases or applies Bayesian or otherstatistical machine learning techniques to construct a personalizedpredictive model that infers the cost of interrupting the user in acontext.
 10. The system of claim 1, the component predicts a probabilityof the relevance of an item or event, or surrogate for relevance thatincludes the probability that a user will attend an event based at leastin part on properties of the item that includes one or more of the eventtime or date, duration, location, subject, number of invitees, or arepetition schedule associated with the event.
 11. The system of claim10, the component further predicts the probability of the user attendingthe event based on one or more hierarchical relationships associatedwith an invitee to the event or the organizer of the event, the one ormore hierarchical relationships dynamically established by thecomponent.
 12. The system of claim 1, component based at least in parton the comparison between the cost for being reminded and the cost forinterrupting the user generates a prognosis associated with live trafficconditions, travel routes, or travel duration associated with attendingthe event.
 13. A machine implemented method that develops or utilizespredictive models of human forgetting and/or remembering to effectuateor facilitate automated reminding, comprising: evaluating an item forrelevance based on a user assessment of the item, contextual informationassociated with the user, or attributes associated with the item;predicting a probability of the user forgetting about the item;aggregating the probability of the user forgetting about the item with auser specific cost to ascertain a cost for not being reminded about theitem; contrasting the cost for not being reminded about the item with acost for interrupting the user about the item; and based at least inpart on the contrasting, disseminating a reminder notification about theitem to the user.
 14. The method of claim 13, further comprisingpersisting feedback from the user and recorded in at least one ofreal-time or offline about whether the reminder notification was atleast one of missing when needed, valuable, or inappropriate, where thereminder notification is indicated as at least one of valuable orinappropriate further soliciting from the user a reason that can includeone or more of the item was not forgotten, too little detail wasdisseminated with the reminder notification, too much detail wasassociated with the reminder notification, the item was irrelevant tothe user, or timing of receipt of the reminder notification wasinappropriate.
 15. The method of claim 13, further comprisingdetermining an appropriate time to interrupt the user and based on theappropriate time for disseminating the reminder notification about theitem to the user.
 16. The method of claim 13, further comprisingutilizing one or more of live traffic conditions, determined travelroutes, or expected travel durations to determine an appropriate time tointerrupt the user and based at least in part on the appropriate time,disseminating the reminder notification about the item to the user, thereminder notification including a proposed departure time, a latestdeparture time, the determined travel routes, or the expected travelduration.
 17. The method of claim 13, further comprising determining aprobability of the user attending an event, the probability of the userattending the event based on an event time or date, an event duration, arepetition cycle associated with the event, a unique location associatedwith the event, or an involvement the user has with the event.
 18. Asystem that utilizes predictive models of human memory to effectuateautomated reminding, comprising: means for assessing an event forrelevance based on a user assessment of the item, contextual informationassociated with the user, or attributes associated with the item; meansfor inferring a probability of the user forgetting about one or moreaspects of the item; means for combining the probability of the userforgetting about the item with a user specific cost to ascertain a costfor not being reminded about one or more aspects of the item; means forcontrasting the cost for not being reminded about one or more aspects ofthe item with a cost for interrupting the user about the event; andmeans for disseminating a reminder notification about the event to theuser.
 19. The system of claim 18, further comprising means for obtaininga user evaluated cost associated with the item, the user evaluated costincludes an avoidance cost that the user assigns to the item.
 20. Thesystem of claim 18, further comprising means for timing the remindernotification based at least in part on a means for determining livetraffic conditions, the means for determining live traffic conditionsprovides dynamic travel route updates, or an expected travel duration,the expected travel duration utilized by the means for disseminating todisseminate the reminder notification.